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The light/dark cycle is the main external cue to synchronize the human biological clock. Modern lifestyles typically lead to less daylight exposure and blunted 24 h-amplitude. We evaluated the association of outdoor daylight exposure (frequency, duration, regularity and shift) with chronotype estimated by sleep phase, regularity of routines, sleep, well-being (WHO-5), and depressive symptoms (PHQ-9), in a sample of 1,095 participants (81.8% female; 87.9% aged 18-49) surveyed online between July and November 2020. We analyzed direct and indirect associations in daylight-mood relationship with chronotype-estimate, routine regularity, and sleep as mediators. Outdoor daylight exposure was associated with WHO-5/PHQ-9 scores in mediation models, with higher total effects when the exposure was every day (ß = 4.13 ± 0.53/ ß = -3.81 ± 0.67), for more than 4 hours (ß = 3.77 ± 0.91/ ß = -3.83 ± 1.31) and during the morning (ß = 3.41 ± 0.53/ ß = -3.74 ± 0.70) in reference to lack of exposure. Chronotype-estimate, routine regularity score, and sleep problems acted as mediators, while social jetlag and sleep duration did not play an important role in this association. This study advanced the understanding of the complex interplay between light exposure, mental health, and individual characteristics of sleep and other routine regularities, and showed the benefits of optimizing daylight exposure to improve mental health.

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BACKGROUND: The Brief Measure of Emotional Preoperative Stress (B-MEPS) was developed to evaluate the preoperative individual vulnerability to emotional stress. To obtain a refined version of B-MEPS suitable for an app approach, this study aimed: (i) to identify items with more discriminant properties; (ii) to classify the level of preoperative emotional stress based on cut-off points; (iii) to assess concurrent validity through correlation with the Central Sensitization Inventory (CSI) score; (iv) to confirm whether the refined version of B-MEPS is an adequate predictive measure for identification of patients prone to intense postoperative pain. METHODS: We include 1016 patients who had undergone surgical procedures in a teaching hospital. The generalized partial credit model of item response theory and latent class model were employed, respectively, to reduce the number of items and to create cut-off points. We applied the CSI and assessed pain by Visual Analog Scale (0-10) and by the amount of postoperative morphine consumption. RESULTS: The refined B-MEPS shows satisfactory reliability (Cronbach's alpha 0.79). Preoperative emotional stress, according to the cut-off points, is classified into categories: low, intermediate or high stress. The refined B-MEPS exhibited a linear association with the CSI scores (r2 = 0.53, p < 0.01). Patients with higher levels of emotional stress displayed a positive association with moderate to severe pain and greater morphine consumption. CONCLUSION: The refined version of B-MEPS, along with an interface of easy applicability, assess emotional vulnerability at the bedside before surgery. This app may support studies focused on intervening with perioperative stress levels.

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OBJECTIVE: To evaluate the diagnostic accuracy of the Radiological Society of North America (RSNA) classification system for coronavirus disease 2019 (COVID-19) pneumonia compared to pre-pandemic chest computed tomography (CT) scan images to mitigate the risk of bias regarding the reference standard. MATERIALS AND METHODS: This was a retrospective, cross-sectional, diagnostic test accuracy study. Chest CT scans, carried out from May 1 to June 30, 2020, and from May 1 to July 17, 2017, were consecutively selected for the COVID-19 (positive reverse transcription-polymerase chain reaction [RT-PCR] for severe acute respiratory syndrome coronavirus 2 result) and control (pre-pandemic) groups, respectively. Four expert thoracic radiologists blindly interpreted each CT scan image. Sensitivity and specificity were calculated. RESULTS: A total of 160 chest CT scan images were included: 79 in the COVID-19 group (56 [43.5-67] years old, 41 men) and 81 in the control group (62 [52-72] years old, 44 men). Typically, an estimated specificity of 98.5% (95% confidence interval [CI] 98.1%-98.4%) was obtained. For the indeterminate classification as a diagnostic threshold, an estimated sensitivity of 88.3% (95% CI 84.7%-91.7%) and a specificity of 79.0% (95% CI 74.5%-83.4%), with an area under the curve of 0.865 (95% CI 0.838-0.895), were obtained. CONCLUSION: The RSNA classification system shows strong diagnostic accuracy for COVID-19 pneumonia, even against pre-pandemic controls. It can be an important aid in clinical decision-making, especially when a typical or indeterminate pattern is found, possibly advising retesting following an initial negative RT-PCR result and streamlining early management and isolation.

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Abstract Objective To evaluate the diagnostic accuracy of the Radiological Society of North America (RSNA) classification system for coronavirus disease 2019 (COVID-19) pneumonia compared to pre-pandemic chest computed tomography (CT) scan images to mitigate the risk of bias regarding the reference standard. Materials and methods This was a retrospective, cross-sectional, diagnostic test accuracy study. Chest CT scans, carried out from May 1 to June 30, 2020, and from May 1 to July 17, 2017, were consecutively selected for the COVID-19 (positive reverse transcription-polymerase chain reaction [RT-PCR] for severe acute respiratory syndrome coronavirus 2 result) and control (pre-pandemic) groups, respectively. Four expert thoracic radiologists blindly interpreted each CT scan image. Sensitivity and specificity were calculated. Results A total of 160 chest CT scan images were included: 79 in the COVID-19 group (56 [43.5-67] years old, 41 men) and 81 in the control group (62 [52-72] years old, 44 men). Typically, an estimated specificity of 98.5% (95% confidence interval [CI] 98.1%-98.4%) was obtained. For the indeterminate classification as a diagnostic threshold, an estimated sensitivity of 88.3% (95% CI 84.7%-91.7%) and a specificity of 79.0% (95% CI 74.5%-83.4%), with an area under the curve of 0.865 (95% CI 0.838-0.895), were obtained. Conclusion The RSNA classification system shows strong diagnostic accuracy for COVID-19 pneumonia, even against pre-pandemic controls. It can be an important aid in clinical decision-making, especially when a typical or indeterminate pattern is found, possibly advising retesting following an initial negative RT-PCR result and streamlining early management and isolation.

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We determined the prevalence of diseases and pathogens associated with mortality in beef cattle in the State of Rio Grande do Sul, Brazil, based on pathology laboratory submissions. Postmortem examinations were conducted on 1,277 beef cattle that died between 2008 and 2018. Information regarding age, time of the year, breed, and regional location were analyzed statistically. Most cattle were from the surrounding region of Porto Alegre, and 78.7% of the analyzed cases had diagnostic value. The diagnostic category with most cases was infectious and/or parasitic diseases (60%), followed by toxic and toxicoinfectious (25%). Most cases occurred in the fall. Major disease conditions identified included hemoprotozoal infection (18.2%), rabies (8.2%), and plant intoxications by Senecio spp. (8.5%) and Pteridium arachnoideum (4.6%). Hemoprotozoal infection occurred at a higher frequency in young cattle, mainly in animals up to 1 y old. Intoxication by Senecio spp. was more frequent in cattle 2-3 y old.

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